Personalized Item Ranking from Implicit User Feedback: A Heterogeneous Information Network Approach

In today’s era of the digital world with information overload, generating personalized recommendations for the e-commerce users is a challenging and interesting problem. Recommendation of top-N items of interest to a user of e-commerce is highly challenging using binary implicit feedback. The training data is usually very sparse and have binary values capturing a user’s action or inaction. Due to the sparseness of data and lack of explicit user preferences, the recommendations generated by model-based and neighborhood-based approaches are not effective. Of late, network-based item recommendation methods, which utilize item related meta-information, are beginning to attract increasing attention for binary implicit feedback data. In this work, we propose a heterogeneous information network based recommendation model for personalized top-N recommendations using binary implicit feedback data. To utilize the potential of meta-information related to items, we utilize the concept of meta-path. To improve the effectiveness of the recommendations, the popularity of items and interest of users are leveraged simultaneously. Personalized weight learning of various meta-paths in the network is performed to determine the intrinsic interests of users from the binary implicit feedback data. To show the effectiveness, the proposed model is experimentally evaluated using the real-world dataset. Available at: https://aisel.aisnet.org/pajais/vol9/iss2/3

WriterForcing: Generating more interesting story endings

We study the problem of generating interesting endings for stories. Neural generative models have shown promising results for various text generation problems. Sequence to Sequence (Seq2Seq) models are typically trained to generate a single output sequence for a given input sequence. However, in the context of a story, multiple endings are possible. Seq2Seq models tend to ignore the context and generate generic and dull responses. Very few works have studied generating diverse and interesting story endings for a given story context. In this paper, we propose models which generate more diverse and interesting outputs by 1) training models to focus attention on important keyphrases of the story, and 2) promoting generation of non-generic words. We show that the combination of the two leads to more diverse and interesting endings.Comment: Accepted in ACL workshop on Storytelling 201

Epidemiology of cardioprotective pharmacological agent use in stable coronary heart disease

AbstractObjectiveTo determine use of class and type of cardioprotective pharmacological agents in patients with stable coronary heart disease (CHD) we performed a prescription audit.MethodsA cross sectional survey was conducted in major districts of Rajasthan in years 2008–09. We evaluated prescription for classes (anti-platelets, β-blockers, angiotensin converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARB), calcium channel blockers (CCB) and statins) and specific pharmacological agents at clinics of physicians in tertiary (n = 18), secondary (n = 69) and primary care (n = 43). Descriptive statistics are reported.ResultsPrescriptions of 2290 stable CHD patients were audited. Anti-platelet use was in 2031 (88.7%), β-blockers 1494 (65.2%), ACE inhibitors 1196 (52.2%), ARBs 712 (31.1%), ACE inhibitors – ARB combinations 19 (0.8%), either ACE inhibitors or ARBs 1908 (83.3%), CCBs 1023 (44.7%), statins 1457 (63.6%) and other lipid lowering agents in 170 (7.4%). Among anti-platelets aspirin–clopidogrel combination was used in 88.5%. Top three molecules in β-blockers were atenolol (37.8%), metoprolol (26.4%) and carvedilol (11.9%); ACE inhibitors ramipril (42.1%), lisinopril (20.3%) and perindopril (10.9%); ARB's losartan (47.7%), valsartan (22.3%) and telmisartan (14.9%); CCBs amlodipine (46.7%), diltiazem (29.1%) and verapamil (9.5%) and statins were atorvastatin (49.8%), simvastatin (28.9%) and rosuvastatin (18.3%). Use of metoprolol, ramipril, valsartan, diltiazem and atorvastatin was more at tertiary care, and atenolol, lisinopril, losartan, amlodipine and simvasatin in primary care (p < 0.01).ConclusionsThere is low use of β-blockers, ACE inhibitors, ARBs and statins in stable CHD patients among physicians in Rajasthan. Significant differences in use of specific molecules at primary, secondary and tertiary healthcare are observed

Auditing of prescriptions in relation to diarrhea in children below 5 years of age: a multicenter study

Background: This study was planned to determine the prescribing pattern of drugs in children below 5 years of age suffering from diarrhea by different categories of doctors in the city of Jaipur (Rajasthan).Methods: This observational retrospective study was conducted in the Pediatric Outpatient Department of SMS Medical College and other hospitals in Jaipur (Rajasthan). In this study, 300 prescription (10% of total prescription) of the children aged below 5 years, suffering from acute diarrhea, were randomly selected.Results: As alone, norfloxacin was noted in 49.2% prescriptions followed by ofloxacin in 24.6% out of 61 prescriptions. In combination, the most common antimicrobial (77.78%) prescribed was norfloxacin with either metronidazole or tinidazole.Conclusions: Antimicrobials should be prescribed rationally for pediatric patients suffering from diarrhea to avoid potential adverse events and increased cost of the treatment . Regular prescription audits in hospitals should be undertaken to promote rational use of drugs

Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

Robotic manipulators are extremely nonlinear complex and, uncertain systems. They have multi-input multi-output (MIMO) dynamics, which makes controlling manipulators difficult. Robotic manipulators have wide applications in many industries like processes, medicine, and space. Effective control of these manipulators is extremely important to perform these industrial tasks. Researchers are working on the control of robotic manipulators using conventional and intelligent control methods. Conventional control methods are proportional integral and derivative (PID), Fractional order proportional integral and derivative (FOPID), sliding mode control (SMC), and optimal & robust control while intelligent control method includes Artificial Neural network (ANN), Fuzzy logic control (FLC) and metaheuristic optimization algorithms based control schemes. This paper presents the trajectory control of a robotic manipulator using a PID controller. Four different meta-heuristic algorithms namely Sooty tern optimization (STO), Spotted Hyena optimizer (SHO), Atom Search optimization (ASO), and Arithmetic Optimization algorithm (AOA) are used to optimize the gains of PID controller for trajectory control of a two-link robotic manipulator and a novel hybrid sooty tern and particle swarm optimization (STOPSO) has been designed. These optimization techniques are nature-inspired algorithms that give the optimal gain values while minimizing the performance indices. A performance index comprising Integral time absolute error (ITAE) having weights for both links has been considered to achieve the desired trajectory. These optimization techniques are stochastic in nature so statistical analysis and Freidman’s ranking test has been performed to evaluate the effectiveness of these algorithms. The proposed hybrid STOPSO provided a fitness value of 0.04541 and showed a standard deviation of 0.0002. A comparative study of these optimization techniques is presented and as a result, hybrid STOPSO provides the best results with minimum fitness value followed by STO, AOA, ASO, and SHO algorithms

Structures and Optical Properties of Anodic Aluminum Oxide Thin Films

Low refractive index materials (n&lt;1.3) are not common in nature. However, they are essential for antireflection coatings. In this study porous anodic aluminum oxide (AAO) on glass substrate was fabricated by electrochemical oxidation and subsequent etching. The pore size was modulated from less than 80 nm to more than 250 nm. The pore depth was controlled by electrochemical anodization and/or chemical etching time. It is challenging to effectively quantify the pore structures and the optical properties of such porous materials. Using spectroscopic ellipsometry, the authors showed that the AAO materials had tunable refractive index from 1.25 to 1.40, which is ideal for antireflection coating on glass (n=1.54). In addition, quantitative information on the AAO film porosity, profile structure, film thickness, dielectric constants, and roughness was also derived from the ellipsometry analysis. It was shown that the as-fabricated AAO film included trace amount of residual metal aluminum with an effective thickness ~0.28 nm